AIMC Topic: Protein Folding

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Scaffolding protein functional sites using deep learning.

Science (New York, N.Y.) Jul 21, 2022
The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without n...
Deep Learning Protein Binding Proteins Protein Engineering Protein Structure, Secondary Protein Folding Catalysis Binding Sites
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Learning Proteome Domain Folding Using LSTMs in an Empirical Kernel Space.

Journal of molecular biology Jun 15, 2022
The recognition of protein structural folds is the starting point for protein function inference and for many structural prediction tools. We previously introduced the idea of using empirical comparisons to create a data-augmented feature space calle...
Protein Domains Algorithms Protein Folding Proteome Neural Networks, Computer
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Protein sequence design with a learned potential.

Nature communications Feb 8, 2022
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network mo...
Computer Simulation Protein Domains Amino Acid Sequence Crystallography, X-Ray Deep Learning Models, Molecular Protein Engineering Protein Folding
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Deep learning methods for 3D structural proteome and interactome modeling.

Current opinion in structural biology Feb 6, 2022
Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in...
Protein Conformation Protein Folding Computational Biology Proteome Deep Learning
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Has DeepMind's AlphaFold solved the protein folding problem?

BioTechniques Feb 4, 2022
DeepMind released AlphaFold 2.0 in 2020, an artificial intelligence model to predict the structure of proteins, which could mean that proteins can be characterized without the need for tedious and costly lab analysis.
Protein Conformation Protein Folding Artificial Intelligence Proteins Models, Molecular
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Harnessing protein folding neural networks for peptide-protein docking.

Nature communications Jan 10, 2022
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been develop...
Protein Folding Protein Interaction Domains and Motifs Protein Conformation, beta-Strand Protein Conformation, alpha-Helical Binding Sites Protein Interaction Mapping Models, Molecular Software Amino Acid Sequence Peptides Protein Binding Proteins Molecular Docking Simulation Neural Networks, Computer
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Protein structures for all.

Science (New York, N.Y.) Dec 16, 2021
AI-powered predictions reveal the shapes of proteins by the thousands.
Software Proteins Artificial Intelligence Protein Conformation Databases, Protein Protein Folding Models, Molecular
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Protein Fold Recognition From Sequences Using Convolutional and Recurrent Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
The identification of a protein fold type from its amino acid sequence provides important insights about the protein 3D structure. In this paper, we propose a deep learning architecture that can process protein residue-level features to address the p...
Deep Learning Computational Biology Algorithms Protein Folding Proteins Sequence Analysis, Protein
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Protein Fold Recognition Based on Auto-Weighted Multi-View Graph Embedding Learning Model.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Protein fold recognition is critical for studies of the protein structure prediction and drug design. Several methods have been proposed to obtain discriminative features from the protein sequences for fold recognition. However, the ensemble methods ...
Sequence Alignment Sequence Analysis, Protein Amino Acid Sequence Protein Folding Proteins Databases, Protein Machine Learning Algorithms Computational Biology
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Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets.

International journal of molecular sciences Nov 27, 2021
The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures; however, it has a prohibitively long inference ...
Protein Structural Elements Amino Acid Sequence Protein Conformation Sequence Alignment Software Design Protein Folding Computational Biology Humans Proteins Neural Networks, Computer
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